title: Automatic construction of 3D-ASM intensity models by simulating image acquisition: application to myocardial gated SPECT studies.
abstract: Active shape models bear a great promise for model-based medical image analysis. Their practical use, though, is undermined due to the need to train such models on large image databases. Automatic building of point distribution models (PDMs) has been successfully addressed and a number of autolandmarking techniques are currently available. However, the need for strategies to automatically build intensity models around each landmark has been largely overlooked in the literature. This work demonstrates the potential of creating intensity models automatically by simulating image generation. We show that it is possible to reuse a 3D PDM built from computed tomography (CT) to segment gated single photon emission computed tomography (gSPECT) studies. Training is performed on a realistic virtual population where image acquisition and formation have been modeled using the SIMIND Monte Carlo simulator and ASPIRE image reconstruction software, respectively. The dataset comprised 208 digital phantoms (4D-NCAT) and 20 clinical studies. The evaluation is accomplished by comparing point-to-surface and volume errors against a proper gold standard. Results show that gSPECT studies can be successfully segmented by models trained under this scheme with subvoxel accuracy. The accuracy in estimated LV function parameters, such as end diastolic volume, end systolic volume, and ejection fraction, ranged from 90.0% to 94.5% for the virtual population and from 87.0% to 89.5% for the clinical population.
authors.affiliation: Center for Computational Imaging and Simulation Technologies in Biomedicine, Universitat Pompeu Fabra, Barcelona 08003, Spain. catalina.tobon@upf.edu
authors.forename: Catalina
authors.initials: C
authors.lastname: Tobon-Gomez
authors.forename: Constantine
authors.initials: C
authors.lastname: Butakoff
authors.forename: Santiago
authors.initials: S
authors.lastname: Aguade
authors.forename: Federico
authors.initials: F
authors.lastname: Sukno
authors.forename: Gloria
authors.initials: G
authors.lastname: Moragas
authors.forename: Alejandro F
authors.initials: AF
authors.lastname: Frangi
mesh_terms: D001185:Artificial Intelligence; D035843:Biomedical Research / Q000379:methods; D055426:Cardiac-Gated Single-Photon Emission Computer-Assisted Tomography / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D001330:Electronic Data Processing / Q000379:methods; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008297:Male; D008955:Models, Cardiovascular; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D012815:Signal Processing, Computer-Assisted; D013318:Stroke Volume; D014057:Tomography, X-Ray Computed / Q000379:methods; D018487:Ventricular Dysfunction, Left / Q000000981:diagnostic imaging / Q000503:physiopathology